83 research outputs found
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review
Molecular and genomic properties are critical in selecting cancer treatments
to target individual tumors, particularly for immunotherapy. However, the
methods to assess such properties are expensive, time-consuming, and often not
routinely performed. Applying machine learning to H&E images can provide a more
cost-effective screening method. Dozens of studies over the last few years have
demonstrated that a variety of molecular biomarkers can be predicted from H&E
alone using the advancements of deep learning: molecular alterations, genomic
subtypes, protein biomarkers, and even the presence of viruses. This article
reviews the diverse applications across cancer types and the methodology to
train and validate these models on whole slide images. From bottom-up to
pathologist-driven to hybrid approaches, the leading trends include a variety
of weakly supervised deep learning-based approaches, as well as mechanisms for
training strongly supervised models in select situations. While results of
these algorithms look promising, some challenges still persist, including small
training sets, rigorous validation, and model explainability. Biomarker
prediction models may yield a screening method to determine when to run
molecular tests or an alternative when molecular tests are not possible. They
also create new opportunities in quantifying intratumoral heterogeneity and
predicting patient outcomes.Comment: 20 pages, 2 figure
Joint and individual analysis of breast cancer histologic images and genomic covariates
A key challenge in modern data analysis is understanding connections between
complex and differing modalities of data. For example, two of the main
approaches to the study of breast cancer are histopathology (analyzing visual
characteristics of tumors) and genetics. While histopathology is the gold
standard for diagnostics and there have been many recent breakthroughs in
genetics, there is little overlap between these two fields. We aim to bridge
this gap by developing methods based on Angle-based Joint and Individual
Variation Explained (AJIVE) to directly explore similarities and differences
between these two modalities. Our approach exploits Convolutional Neural
Networks (CNNs) as a powerful, automatic method for image feature extraction to
address some of the challenges presented by statistical analysis of
histopathology image data. CNNs raise issues of interpretability that we
address by developing novel methods to explore visual modes of variation
captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features.
Our results provide many interpretable connections and contrasts between
histopathology and genetics
Appearance normalization of histology slides
This paper presents a method for automatic color and intensity normalization of digitized histology slides stained with two different agents. In comparison to previous approaches, prior information on the stain vectors is used in the plane estimation process, resulting in improved stability of the estimates. Due to the prevalence of hematoxylin and eosin staining for histology slides, the proposed method has significant practical utility. In particular, it can be used as a first step to standardize appearance across slides and is effective at countering effects due to differing stain amounts and protocols and counteracting slide fading. The approach is validated against non-prior plane-fitting using synthetic experiments and 13 real datasets. Results of application of the method to adjustment of faded slides are given, and the effectiveness of the method in aiding statistical classification is shown
In Vivo T Cell Costimulation Blockade with Abatacept for Acute Graft-versus-Host Disease Prevention: A First-in-Disease Trial
AbstractWe performed a first-in-disease trial of in vivo CD28:CD80/86 costimulation blockade with abatacept for acute graft-versus-host disease (aGVHD) prevention during unrelated-donor hematopoietic cell transplantation (HCT). All patients received cyclosporine/methotrexate plus 4 doses of abatacept (10 mg/kg/dose) on days −1, +5, +14, +28 post-HCT. The feasibility of adding abatacept, its pharmacokinetics, pharmacodynamics, and its impact on aGVHD, infection, relapse, and transplantation-related mortality (TRM) were assessed. All patients received the planned abatacept doses, and no infusion reactions were noted. Compared with a cohort of patients not receiving abatacept (the StdRx cohort), patients enrolled in the study (the ABA cohort) demonstrated significant inhibition of early CD4+ T cell proliferation and activation, affecting predominantly the effector memory (Tem) subpopulation, with 7- and 10-fold fewer proliferating and activated CD4+ Tem cells, respectively, at day+28 in the ABA cohort compared with the StdRx cohort (P < .01). The ABA patients demonstrated a low rate of aGVHD, despite robust immune reconstitution, with 2 of 10 patients diagnosed with grade II-IV aGVHD before day +100, no deaths from infection, no day +100 TRM, and with 7 of 10 evaluable patients surviving (median follow-up, 16 months). These results suggest that costimulation blockade with abatacept can significantly affect CD4+ T cell proliferation and activation post-transplantation, and may be an important adjunct to standard immunoprophylaxis for aGVHD in patients undergoing unrelated-donor HCT
Detection of gene orthology from gene co-expression and protein interaction networks
Background Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families.
Results We exploit features derived from the alignment of protein-protein interaction networks and gene-coexpression networks to reconstruct KEGG orthologs for Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository and Mus musculus and Homo sapiens and Sus scrofa gene coexpression networks extracted from NCBI\u27s Gene Expression Omnibus using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms.
Conclusions The performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit
Priorities for synthesis research in ecology and environmental science
ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD
Priorities for synthesis research in ecology and environmental science
ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD
Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe
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